Abstract
Increasingly, routine surveillance and monitoring of foodborne pathogens using whole-genome sequencing is creating opportunities to study foodborne illness epidemiology beyond routine outbreak investigations and case–control studies. Using a global phylogeny of Salmonella enterica serotype Typhimurium, we found that major livestock sources of the pathogen in the United States can be predicted through whole-genome sequencing data. Relatively steady rates of sequence divergence in livestock lineages enabled the inference of their recent origins. Elevated accumulation of lineage-specific pseudogenes after divergence from generalist populations and possible metabolic acclimation in a representative swine isolate indicates possible emergence of host adaptation. We developed and retrospectively applied a machine learning Random Forest classifier for genomic source prediction of Salmonella Typhimurium that correctly attributed 7 of 8 major zoonotic outbreaks in the United States during 1998–2013. We further identified 50 key genetic features that were sufficient for robust livestock source prediction.
Highlights
Routine surveillance and monitoring of foodborne pathogens using whole-genome sequencing is creating opportunities to study foodborne illness epidemiology beyond routine outbreak investigations and case–control studies
Population Structure We constructed a maximum-likelihood phylogeny of the 1,267 isolates based on single-nucleotide polymorphisms (SNPs) identified in the core genome alignments (Figure 1, panel A)
Human clinical and miscellaneous food isolates were widely distributed among population groups; clinical and food isolates were found in every group, except we found no food isolates in G6
Summary
Routine surveillance and monitoring of foodborne pathogens using whole-genome sequencing is creating opportunities to study foodborne illness epidemiology beyond routine outbreak investigations and case–control studies. Using a global phylogeny of Salmonella enterica serotype Typhimurium, we found that major livestock sources of the pathogen in the United States can be predicted through whole-genome sequencing data. We developed and retrospectively applied a machine learning Random Forest classifier for genomic source prediction of Salmonella Typhimurium that correctly attributed 7 of 8 major zoonotic outbreaks in the United States during 1998–2013. Salmonella enterica is one of the most prevalent foodborne pathogens worldwide, causing >1 million human cases and an economic burden of $3.7 billion annually in the United States alone [1,2]. We investigated zoonotic source attribution of Salmonella Typhimurium under an extensive phylogenomic framework by including a large collection of isolates from 3 major US laboratory surveillance and monitoring programs. Machine learning enabled comprehensive and high-resolution screening for key genetic indicators of source association throughout Salmonella Typhimurium genomes
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